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  3. A Multi-controller Embedded Intelligent Crane System With Integrated Fire Safety For Light-load Material Handling.
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  3. A Multi-controller Embedded Intelligent Crane System With Integrated Fire Safety For Light-load Material Handling.

Related Experiment Video

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

A Multi-Controller Embedded Intelligent Crane System with Integrated Fire Safety for Light-Load Material Handling.

Zhangwen Huang1, Jiayang Song1, Yuxiang Shi1

  • 1Trine Engineering Institute, Shaanxi University of Technology, Hanzhong 723000, China.

Sensors (Basel, Switzerland)
|May 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an Arduino-based intelligent crane system for material handling, enhancing operational efficiency by 29.6% and integrating crucial fire safety features for smarter warehouses.

Keywords:
Arduinoembedded controlfire emergency responseintelligent crane systemmodular controlmulti-sensor fusionpath planning

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11:53

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Published on: October 14, 2017

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10:09

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Area of Science:

  • Industrial Engineering
  • Robotics
  • Automation Systems

Background:

  • Traditional material handling systems lack flexibility and integrated safety features.
  • Industrial intelligence demands advanced solutions for material handling.
  • Existing systems exhibit weak fire safety responses.

Purpose of the Study:

  • To design an Arduino-based intelligent material handling crane system.
  • To integrate multifunctional capabilities including environmental perception and path planning.
  • To incorporate dual fire safety protection: smoke alarm and automatic fire extinguishing.

Main Methods:

  • Development of a modular multi-sensor fusion architecture.
  • Implementation using an Arduino platform for control.
  • Experimental validation in a controlled laboratory environment.

    • Main Results:

    • Achieved a 29.6% improvement in object handling operational efficiency.
    • Successfully demonstrated environmental perception and automatic path planning.
    • Integrated and tested dual fire safety protection mechanisms.

    Conclusions:

    • The prototype system offers an experimental reference for intelligent upgrades.
    • The system enhances both efficiency and safety in material handling.
    • Addresses limitations of traditional systems in industrial intelligence contexts.